Health AI Central
Share:
Peer-Reviewed Publication
PLoS One2026;21(3):e0343492.January 1, 2026Journal Article

Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules.

Caroline M Godfrey1, Ashley A Leech2, Kevin C McGann1, Jinyi Zhu2, Hannah N Marmor3, Sophia Pena3, Lyndsey C Pickup4, Fabien Maldonado5, Evan C Osmundson6, Stacie B Dusetzina2, Eric L Grogan3,7, Stephen A Deppen3,7
1Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
2Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
3Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
4Optellum Ltd, Oxford, United Kingdom.
5Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
6Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
7Tennessee Valley Healthcare System, Nashville, Tennessee, United States of America.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Artificial intelligence-based radiomic approaches have been shown to accurately evaluate indeterminate pulmonary nodules. With the expansion of lung cancer screening and utilization of computed tomography imaging, indeterminate pulmonary nodules requiring diagnostic evaluation are increasingly common. Accurate non-invasive characterization may reduce time to cancer diagnosis and decrea…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.